Use the regression model to make predictions for Year 10. Note that the regression model coefficients are stored below the data table, highlighted in green.

\begin{tabular}{|c|c|c|c|c|c|c|c|c|} \hline \multicolumn{9}{|l|}{ SUMMARY OUTPUT } \\ \hline \multicolumn{9}{|l|}{ Regression Statistics } \\ \hline Multiple R & 0.712218552 & & & & & & & \\ \hline R Square & 0.507255266 & & & & & & & \\ \hline Adjusted R Square & 0.457980793 & & & & & & & \\ \hline Standard Error & 728.4380659 & & & & & & & \\ \hline Observations & 56 & & & & & & & \\ \hline \multicolumn{9}{|l|}{ ANOVA } \\ \hline & df & SS & MS & F & Significance F & & & \\ \hline Regression & 5 & 27312398.64 & 5462479.727 & 10.29448376 & 7.9889E07 & & & \\ \hline Residual & 50 & 26531100.79 & 530622.0159 & & & & & \\ \hline \multirow[t]{2}{*}{ Total } & 55 & 53843499.43 & & & & & & \\ \hline & Coefficients & Standard Error & t Stat & P-value & Lower 95\% & Upper 95% & Lower 95.0% & Upper 95.0% \\ \hline Intercept & 1888.045086 & 1630.501771 & 1.15795341 & 0.25238446 & -1386.914103 & 5163.004275 & -1386.914103 & 5163.004275 \\ \hline Week In Season & -191.5569628 & 30.01812904 & -6.381375819 & 5.57102E08 & -251.8501494 & -131.2637762 & -251.8501494 & -131.2637762 \\ \hline Opponent Preseason Rank & -7.450143771 & 3.073344242 & -2.424116267 & 0.019004114 & -13.62313735 & -1.277150189 & -13.62313735 & -1.277150189 \\ \hline Preseason Ticket Sales & 0.056551189 & 0.031246239 & 1.80985587 & 0.076330521 & -0.006208729 & 0.119311106 & -0.006208729 & 0.119311106 \\ \hline CSU Preseason Rank & 0.061592234 & 6.125078609 & 0.010055746 & 0.992016827 & -12.24099022 & 12.36417469 & -12.24099022 & 12.36417469 \\ \hline Throwback Jersey (1= Yes; 0= No) & 732.2201265 & 760.3519488 & 0.963001578 & 0.340181176 & -794.9917087 & 2259.431962 & -794.9917087 & 2259.431962 \\ \hline \end{tabular}